Methods for optimizing the planning and placement of probes in the brain via multimodal 3d analyses of cerebral anatomy

ABSTRACT

A method includes obtaining a first imaging scan and a second imaging scan of a single subject brain. The first imaging scan is converted to a first dataset, and the second imaging scan is converted to a second dataset. A sequence-adaptive multimodal segmentation algorithm is applied to the first dataset and the second dataset. The sequence-adaptive multimodal segmentation algorithm performs automatic intensity-based tissue classification to generate a first labelled dataset and a second labeled dataset. The first labeled dataset and the second labeled dataset are automatically co-registered to each other to generate a transformation matrix based on the first labeled dataset and the second labeled dataset. The transformation matrix is applied to align the first dataset and the second dataset.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority to U.S. Provisional PatentApplication No. 62/978,868, filed Feb. 20, 2020, entitled “Methods ofIdentifying and Avoiding Visual Deficits After Laser InterstitialThermal Therapy for Mesial Temporal Lobe Epilepsy,” which is herebyincorporated herein by reference in its entirety.

BACKGROUND

Intracranial electrodes are implanted in patients for the recording ofhigh spatiotemporal resolution intracranial electroencephalography(icEEG) data and for the modulation of neural circuits and systems.Patients undergo implantation most commonly for the evaluation ofneurological diseases such epilepsy, movement disorders and psychiatricillnesses.

In a subset of patients with medically intractable epilepsy, seizureonsets can be localized to a definable focus, and in such exemplarycircumstances, surgical interventions offer a potential for thecessation of further seizure activity through the direct resection,removal, or destruction of the pathological brain tissue, includingminimally invasive approaches that include, but are not limited to,catheter-based tissue ablation. Unfortunately, in many cases, patientsdo not have a lesion that is identifiable using only non-invasivestudies or evaluations, which can include studies of brain activity thatinclude, but are not limited to, scalp electroencephalography (EEG) andmagneto-encephalography (MEG), as well as anatomical imaging modalitiesused to identify structural lesions such as magnetic resonance imaging(MRI) or computed tomography (CT). Electrodes are also placed for theneuromodulation of epilepsy—currently that is either in the anteriornucleus of the thalamus or at the site of the genesis of seizures.

Movement disorders (Parkinson's disease, dystonia, essential tremor) arealso common. The treatment of these disorders is often surgical, asmedications generally result in undesirable side effects. Deep basalganglia nuclei (e.g. subthalamic nucleus, globus pallidus interna andthe VIM nucleus of the thalamus) and their associated white matterpathways are routinely targeted for these disorders.

Psychiatric disorders are rapidly becoming targeted for neuromodulationin cases where medications are ineffective—these include cases oftreatment resistant depression, obsessive compulsive disorder,post-traumatic stress disorders and eating disorders.

In such exemplary patients, implantation of subdural electrodes (SDEs)and/or stereo-electroencephalography (SEEG) electrodes and/or otherprobes or catheters or recording devices is a common strategy used toprecisely define the relationships between healthy and/or eloquent brainregions and the pathologic brain regions that may underlie a putativepathological network, for diagnosis or for stimulation to causeneuromodulation.

SUMMARY

A method includes obtaining a first imaging scan and a second imagingscan of a single subject brain. The first imaging scan is converted to afirst dataset, and the second imaging scan is converted to a seconddataset. A sequence-adaptive multimodal segmentation algorithm isapplied to the first dataset and the second dataset. Thesequence-adaptive multimodal segmentation algorithm performs automaticintensity-based tissue classification to generate a first labelleddataset and a second labeled dataset. The first labeled dataset and thesecond labeled dataset are automatically co-registered to each other togenerate a transformation matrix based on the first labeled dataset andthe second labeled dataset. The transformation matrix is applied toalign the first dataset and the second dataset.

A non-transitory computer-readable medium encoded with instructions thatare executable by one or more processors to obtain a first imaging scanand a second imaging scan of a single subject brain, and to convert thefirst imaging scan to a first dataset, and the second imaging scan to asecond dataset. The instructions are also executable by the one or moreprocessors to apply a sequence-adaptive multimodal segmentationalgorithm to the first dataset and the second dataset, wherein thesequence-adaptive multimodal segmentation algorithm performs automaticintensity-based tissue classification to generate a first labelleddataset and a second labeled dataset. The instructions are furtherexecutable by the one or more processors to automatically co-registerthe first labeled dataset and the second labeled dataset to each otherto generate a transformation matrix based on the first labeled datasetand the second labeled dataset. The instructions are yet furtherexecutable by the one or more processors to apply the transformationmatrix to align the first dataset and the second dataset.

A system includes one or more processors and a memory. The memory iscoupled to the one or more processors, and stores instructions. Theinstructions configure the one or more processors to obtain a firstimaging scan and a second imaging scan of a subject brain, and toconvert the first imaging scan to a first dataset, and the secondimaging scan to a second dataset. The instructions also configure theone or more processors to apply a sequence-adaptive multimodalsegmentation algorithm to the first dataset and the second dataset,wherein the sequence-adaptive multimodal segmentation algorithm performsautomatic intensity-based tissue classification to generate a firstlabelled dataset and a second labeled dataset. The instructions furtherconfigure the one or more processors to automatically co-register thefirst labeled dataset and the second labeled dataset to each other togenerate a transformation matrix based on the first labeled dataset andthe second labeled dataset. The instructions yet further configure theone or more processors to apply the transformation matrix to align thefirst dataset and the second dataset.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of various examples, reference will now bemade to the accompanying drawings in which:

FIG. 1 shows a flow diagram for a method for co-registration of brainimaging scans in accordance with the present disclosure.

FIG. 2 shows a flow diagram for a method for generation of surfacemodels of cortical and subcortical brain regions in accordance with thepresent disclosure.

FIG. 3 shows a flow diagram for a method for automated segmentation ofbrain vasculature in accordance with the present disclosure.

FIG. 4 shows a flow diagram for a method for visualizing underlyingbrain structures in accordance with the present disclosure.

FIG. 5 shows a flow diagram for a method for automated planning ofelectrode or probe implantation in accordance with the presentdisclosure.

FIG. 6 shows a flow diagram for a method for automated localization,naming, and visualization of previously implanted electrodes orpenetrating brain probes in accordance with the present disclosure.

FIG. 7 shows a pictorial representation of co-registration of differentneuroimaging modalities performed on a single subject in accordance withthe present disclosure.

FIGS. 8A-8F show a pictorial representation depicting generation of2D/3D surface models of the hippocampus as well as the thalamus inaccordance with the present disclosure.

FIGS. 9A-9D show example steps for segmentation of humancerebrovasculature in accordance with the present disclosure.

FIGS. 10A-10N and 10P-10W show use of a cutting plane to intersect withsurface and volume models at arbitrary angles to optimize thevisualization of cortical and subcortical structural and/or functionalrepresentations in accordance with the present disclosure.

FIGS. 11A-11H, 11J-11N, and 11P-11R show an example ofpopulation-derived anatomical targeting for electrode or penetratingprobe implantation in accordance with the present disclosure.

FIGS. 12A-12E show pictorial representations of automated electrodelocalization and labelling.

FIG. 13 shows a block diagram for a computing system suitable forimplementation of the methods disclosed herein.

DETAILED DESCRIPTION

Certain terms have been used throughout this description and claims torefer to particular system components. As one skilled in the art willappreciate, different parties may refer to a component by differentnames. This document does not intend to distinguish between componentsthat differ in name but not function. In this disclosure and claims, theterms “including” and “comprising” are used in an open-ended fashion,and thus should be interpreted to mean “including, but not limited to .. . .” Also, the term “couple” or “couples” is intended to mean eitheran indirect or direct wired or wireless connection. Thus, if a firstdevice couples to a second device, that connection may be through adirect connection or through an indirect connection via other devicesand connections. The recitation “based on” is intended to mean “based atleast in part on.” Therefore, if X is based on Y, X may be a function ofY and any number of other factors.

There are a number of clinical justifications for implanting electrodesor other medical devices in the brain, and focal epilepsy provides asingle exemplary condition, that is focused on herein for illustrativepurposes. In all such patients that meet the criteria for invasivemonitoring or interventional procedure, the patient's clinical coursecan be broadly classified into three stages: 1) planning; 2) dataacquisition; and 3) intervention. In general, during planning stages,patients undergo high-resolution anatomical imaging using MRI, which mayalso be performed following injection of a contrast agent into the bloodstream for the enhanced imaging of blood vessels. More recently, withimprovements in computational modeling technologies, data from theseimaging scans may be used to generate 2D and 3D models of the patient'scerebral anatomy, which can better inform surgeons and cliniciansplanning subsequent surgical interventions while accounting for criticalor vital anatomical structures.

Following planning, the patients will undergo intracranial electrodeimplantation, following which post-operative imaging is typicallyacquired (e.g. CT brain scan) in order to precisely and accuratelyrelate the implanted electrodes to the patient's cortical anatomy.Similarly, 2D and/or 3D computational models of the implanted electrodesor probes can be generated from the repeat imaging scans, and used torelate the electrophysiological data acquired to the underlying corticalanatomy once the different (pre- and post-implant) imaging data arebrought into co-registration with each other (e.g. aligned to a commoncoordinate space).

In the intervention stage, the data gathered from stages 1 and 2(planning and implantation) have been used as part of the patient'scomprehensive clinical evaluation to determine whether a putativepathological focus can be localized, and if so, what its relationshipsto critical and non-critical brain structures may be. This informationis used by the surgeon to optimize a final surgical plan for the removalof seizure foci that minimizes injury to healthy or vital brain regions.

In each of these stages, precision and accuracy are paramount to ensurethat the patient does not sustain any transient or permanent adverseneurological outcome that could have otherwise been avoided. Despite thenumerous technical, imaging, and computational advances that have beendeveloped in the past 20 years, significant technological hurdlesremain. These include challenges for accurate co-registration ofdifferent imaging modalities; for the automated planning of electrodeimplantation; for methods using non-invasive imaging data only tominimize risk of injury to critical structures (e.g. blood vessels); forthe automated and/or semi-automated integration of post-implantationimaging data with neuro-anatomical and functional data to informpotential surgical interventions. The methods and systems disclosedherein overcome the aforementioned limitations using the novelapproaches described below.

Embodiments of the present disclosure relate to the robust and accurateco-registration of different brain imaging modalities. In particular,the present disclosure describes a novel application of asequence-adaptive multimodal segmentation algorithm to the same and/ordifferent imaging modalities used for the acquisition of brain imagingto achieve accurate within-subject multi-modal co-registration of theseimaging modalities in a robust and automated fashion. Thesequence-adaptive multimodal segmentation algorithm is applied to 3Ddatasets generated from the original brain imaging scans performed onthe patient, which may include but are not limited to data formats asdescribed by the Neuroimaging Informatics Technology Initiative (NIFTI),and which are generated from the imaging files of patient's brain scan,an exemplary embodiment of which could be images stored according to thestandards defined by the Digital Imaging and Communications in Medicine(DICOM) standard. The sequence-adaptive multimodal segmentationalgorithm is applied to the datasets without the need of further oradditional pre-processing (including but not limited to intensitynormalization and/or volume conformation) to generate a new labeledsegmentation dataset of the same volume and geometry of the originaldatasets but one in which each voxel (i.e., 3D pixel) has had itsoriginal intensity value replaced by a number that relates to a uniqueanatomical region and/or the probability of that voxel belonging tovarious brain regions (an exemplary embodiment of which may include aprobabilistic distribution of brain regions as defined by a template orreference atlas). The segmented dataset is then utilized as the “moving”input to the co-registration algorithm to be aligned to an equivalentlysegmented “target” dataset generated from the same and/or differentimaging modality on the same patient. The co-registration algorithm willalso generate as part of the computational output a transformationmatrix that describes the mathematical operations needed to exactlyreplicate the alignment between the input “moving” and “target” datasetsin a symmetric fashion—that is in both a forward (i.e., align the“moving” dataset to the “target” dataset) and a backward (i.e., alignthe “target” dataset to the “forward” dataset) fashion. Once thetransformation matrix is generated, the transformation matrix can beapplied to any dataset that shares the volume geometry of the originalmoving dataset to bring it into alignment with the original targetdataset. This disclosure describes an entirely new application ofsegmentation algorithms to co-register imaging datasets taken in thesame subject using the same or different imaging modalities (e.g. MRIand CT) based on transformation matrices generated using segmentation,which provides a technological improvement that significantly advancesthe prior state of the art for within-subject multi-modalco-registration of brain images. Implementations of this disclosureresult in satisfactory outcomes despite the presence of imaging featuresthat would be a common cause of failure in other currently existingco-registration methods, which include but are not limited to anatomicaldefects, lesions and/or masses, foreign bodies, hemorrhage and/orintensity differences between imaging datasets and/or differences in theimaging pulse-sequence used and/or scanner platform parameter on whichthe image was acquired.

Embodiments of the present disclosure relate to methods of usingextracranial boundary layers, generated by a sequence-adaptivemultimodal segmentation algorithm to generate anatomically accurate skinand skull models to facilitate the pre-surgical planning andpost-implantation localization of sEEG electrodes. The segmentation ofextracranial boundary elements directly from CT imaging is an entirelynovel approach that is an improvement on prior methods. The generationof a skull boundary layer using the methods applied to a T1-weighted MRIdataset are also entirely new approaches to the field that demonstrateimprovements to current methods of boundary element modelling used toestimate these layers. These improvements provide tangible benefits tothe planning of surgical electrode implantation by providing theskull-brain and skin-skull boundary layer for electrode implantation.

Embodiments of the present disclosure relate to methods of using thesegmentations of cerebrospinal fluid (CSF) volumes, as well as gray andwhite matter cortical layers, as masks to aid in the segmentation ofblood vessels from 3D brain imaging datasets which include but are notlimited to contrast-weighted T1 MR imaging. In some exemplaryembodiments, MR imaging datasets may have their intensity valuesup-scaled so as to better separate high-intensity voxels likely toreflect blood vessels from surrounding tissues with similar (but lower)intensity values (e.g., white matter tracts or from partial-volumeaveraging). The use of CSF boundary layers to mask and constrain theparameter space for blood vessel segmentation using up-scaled contrastMRIs is a novel approach for facilitating the segmentation of cerebralvasculature.

Embodiments of the disclosure relate to methods of applying, in someexemplary embodiments, a multi-scale Hessian-based filter to segmentblood-vessels from the aforementioned CSF-, gray-, andwhite-matter-masked datasets.

Embodiments of the present disclosure relate to methods of generating2D/3D anatomical mesh models of a patient's hippocampus, amygdala, aswell as other subcortical structures, and for generating standardizedversions of these anatomical meshes derived from high-resolutiontemplate volumes of the same.

Embodiments of the present disclosure relate to methods of determiningoptimal trajectories for the implantation of electrodes or penetratingprobes into the brain using loss-functions and/or risk metrics definedin relation to the segmented blood vessel volumes.

Embodiments of the present disclosure relate to methods of 2D/3Dvisualization of cerebral vasculature. In some exemplary embodiments,the aforementioned visualizations will require reconstructingdiscontinuities in blood vessel volumes. In some exemplary embodiments,this visualization is achieved using algorithms originally developed fordiffusion tensor imaging. In such exemplary embodiments, digital imageintensity modulation is applied to the blood vessel volumes, constrainedto specific directional gradients in 3D space that in some embodimentsis performed using Hessian or Tensor based decompositions, to mimicanisotropic diffusion in 3D imaging volumes. These datasets cansubsequently be processed by diffusion tensor imaging toolboxes to modeland predict the connections between similar yet discontinuous imagingfeatures. In this fashion, for example, likely connections betweensimilar voxels that have become discontinuous due to low signal-to-noiseratios or processing artifacts can be re-modeled and visualized (e.g.for blood vessels in 3D imaging volumes). In this fashion, for example,continuous blood vessels can be reconstructed from discontinuousdatasets.

Embodiments of the present disclosure relate to methods of generatingtopologically accurate 2D/3D surface-based representations and/oranatomical mesh models of the hippocampus, amygdala, thalamus, basalganglia, and other subcortical structures for use in surgical planning.Parcellations of cortical regions are also generated, and any one ofthese structures can be visualized and manipulated independently, withrespect to adjacent structures and contralateral structures. Further,using population-based atlases and templates, standardized surfacemodels of these subcortical and/or other deep structures may also begenerated, which will enable the translation and application ofsurface-based co-registration and analyses techniques that wereoriginally developed for cortical-based inter-subject analyses and shownto yield significant improvements to accuracy and results. These methodsare an entirely new contribution to the field which will providesignificant improvements in the modeling of hippocampal and/or othersubcortical region pathology or understanding of hippocampal/amygdala orother deep brain structure pathology through direct visualization orthrough the representation of functional or electrographic data upon themesh surface model, or for the modeling of electrode or penetratingprobe or catheter implantation to these regions.

Embodiments of the present disclosure relate to methods of aligningtrajectories, obtained from a robotic, mechanical, or human implantationdevice or process, to the subject anatomical T1 MR imaging using thevolume geometry and transformation matrices obtained from the subject'scontrast-weighted T1 MR imaging.

Embodiments of the present disclosure relate to methods of preciselyidentifying the anatomical targets to be implanted or targeted usingautomated parcellation techniques, as well as linear and/or non-lineardeformation algorithms to warp the subject brain to a standard templatespace in which targets are previously defined based on anatomy andvice-versa.

Embodiments of the present disclosure relate to methods of preciselyidentifying the anatomical target to be implanted or targeted using aprior probability distribution derived from a previously implantedpopulation.

Embodiments of the present disclosure relate to assigning anatomicaltargets to motivate unsupervised design of implantation trajectoriesusing depth electrodes placed for the identification of epilepsydirected by specific semiological features of seizures or thecharacterization of the epilepsy. This may apply to the placement oflaser probes, brain electrodes for recording or for modulation,stereotactic biopsy probes, injection of biological, cellular, geneticor chemical materials into the brain via trajectories using anatomicalconstraints and prior implant cases.

Embodiments of the present disclosure relate to methods of generating 3Dsurface models of predicted ablation volumes (i.e. expected volume ofhippocampal tissue to be affected) using a prior probabilitydistribution derived from previous laser ablation volumes across apopulation. This is a new contribution to the field that will improvethe pre-surgical planning and informed trajectory modeling forsubsequent laser-ablation or other similar catheter-based therapies.

Embodiments of the present disclosure relate to automated techniques toidentify and avoid damage to white matter pathways (identified viaeither deterministic or probabilistic tractography derived fromdiffusion imaging) involved in crucial functions such as motor, sensory,auditory, or visual processes.

Embodiments of the present disclosure relate to methods of automatedsegmentation and localization of sEEG electrodes usingintensity-upscaling of post-implantation CT Brain imaging datasets andvolume-based clustering algorithms of the resulting metal artifacts.Trajectories from the robotic implantation device, previously aligned tothe same imaging space, are fitted using linear regression modeling tofacilitate the identification of metal electrode artifacts from noise.Line-fitting models enable the automated method to account fordeviations in electrode locations. Artifact clusters identified using 3Dvolumetric clustering search algorithms are iteratively searched whilemasking out any cortical region not within the current cluster ofinterest, to ensure overlapping or conflated artifacts can be resolved.Identified clusters are aligned to the robotic trajectories usinginformation about the parallel relationships between the trajectory pathand trajectories of the identified clusters, as well as informationabout the centroid of the clusters to re-fit the trajectories oncesufficient numbers of electrodes are identified. Real-time visualizationof the search algorithm enables concurrent updates of cluster searchresults for informational and debugging purposes.

Embodiments of the present disclosure relate to methods for verificationof an implantation plan of multiple stereotactic depth probes alongoblique trajectories, which is aided by simultaneous visualization of acortical mesh model of surface topology and deep anatomy revealed bystructural magnetic resonance imaging, sliced along a plane colinearwith the proposed trajectory. The slicing of the brain surface in anygiven plane enables the visualization of deep buried cortex in 3D andalso enables rapid confirmation of the surgical plan by clinicians.

Embodiments of the present disclosure relate to methods of manipulating3D spatial geometry to selectively visualize or interact with differingsurface models (e.g. skin, skull, blood vessel, brain, hippocampus,amygdala, etc.) along any plane. Surfaces can be rendered selectivelytraversable along any plane. Visualizations of parcellations orintracranial EEG activity can likewise be rendered along or deep to thevisualized surfaces, along any plane.

Embodiments of the present disclosure relate to methods for transformingintracranial electroencephalography (icEEG) activity from surface-basedrepresentations to DICOM or 3D dataset formats, which depict theuser-defined activations of interest constrained to the underlyingcortical ribbon reflecting the likely gray matter regions responsiblefor this activation.

Embodiments of the present disclosure relate to methods for performingempirical source-localization estimates to model the unique neuralgenerators of the recorded icEEG data.

Using the methods disclosed herein, implanted structures are resolvedautomatically with a template matching search in post-surgical imagingalong known planned trajectories. This provides clinical staff with thefinal location for all implanted materials in automated fashion (withoutmanually identifying each electrode for example) and enables therigorous measurement of surgical accuracy. In a specific application,the methods disclosed herein help to avoid visual deficits after laserinterstitial thermal therapy for mesial temporal lobe epilepsy.

FIG. 1 shows a flow diagram for a method 100 for co-registration ofbrain imaging scans obtained for a subject by different imagingmodalities. Though depicted sequentially as a matter of convenience, atleast some of the actions shown can be performed in a different orderand/or performed in parallel. Additionally, some implementations mayperform only some of the actions shown. Operations of the method 100 maybe performed by a computing system as disclosed herein.

In block 102, one or more imaging scans of a subject's brain areobtained. The imaging scans may be performed with a scanning modalitysuch as magnetic resonance imaging sequences (MRI), computerizedtomography (CT) sequences, magnetoencephalography (MEG), positronemission tomography (PET), or any combination thereof.

In block 104, the imaging scans are converted into a file format/datasetthat can be used for storage, analysis, and manipulation of the brainimaging data contained within the imaging scan. For example, the imagingscans may be converted to the NIFTI format.

In block 106, each dataset produced in block 104 is provided as input toa sequence-adaptive multimodal segmentation algorithm to generatelabeled parcellation and segmentation datasets. In general, thesegmentation algorithm proceeds by aligning a probabilistic atlas to thepatient dataset. The atlas, in one exemplary embodiment, helps assignthe probability of a specific brain region label to any voxel of thedataset given the intensity value of the voxel. In one exemplaryembodiment, this could be achieved by using a Bayesian analysis wherethe atlas provides prior probabilities of a voxel belonging to aspecific tissue class. The algorithm may then utilize, as an example,likelihood distributions to define relationships between a given brainregion label and the distribution of intensity values in the voxels ofthat dataset. The term tissue classifications may refer to white matter,gray matter, cerebrospinal fluid, brain tumor and/or other brainregions. The term alignment used here may refer to either linear ornon-linear methods. For examples of the use of a segmentation algorithmfor the sequence-adaptive segmentation of brain MRI see, for example:Puonti O., Iglesias J. E., Van Leemput K. (2013) Fast, Sequence AdaptiveParcellation of Brain MR Using Parametric Models. In: Mori K., SakumaI., Sato Y., Barillot C., Navab N. (eds) Medical Image Computing andComputer-Assisted Intervention—MICCAI 2013. MICCAI 2013. Lecture Notesin Computer Science, vol 8149. Springer, Berlin, Heidelberg.https://doi.org/10.1007/978-3-642-40811-3_91. Application of thesequencing algorithm for the co-registration of different imagingmodalities is unknown in the art (e.g. between MRI and CT), and is anovel application here. Notably, the application of this algorithm toachieve fast, accurate, and robust co-registration between differentimaging modalities (an exemplary embodiment of which may beco-registration of MRI and CT imaging scans) is a significantimprovement over the prior state of the art. In the labeled datasets,imaging data (an exemplar data unit of imaging data is a 3D pixel of 1mm×1 mm×1 mm resolution (referred to herein as a voxel), are replaced bynumeric labels assigned according to extra- and intra-cranial structuresrepresented. In various embodiments, a voxel can be of any dimensionsdeemed useful by the user. the term parcellations are used to refer tolabelled cortical regions, while the term segmentations refer tolabelled subcortical regions. In the present disclosure, these two termsare used interchangeably to refer to any labeled cortical or subcorticalregion.

in block 108, the labeled datasets are input to a co-registrationalgorithm whereby any two datasets are aligned to the coordinate spaceof each other, and from which a mathematical transformation matrix isgenerated that enables this coordinate transformation to be applied toany other dataset that shares the volume geometry of either one of thetwo input datasets in order to align this other dataset to thecoordinate space of the second input dataset. The transformation matrixmay be, in one exemplary embodiment, a 4×4 matrix, M, that defines alinear, rigid, and/or affine transformation of a point p to anotherpoint p′ as defined by the equation p′=Mp. In this example, the point pdefines the location of one voxel in a dataset, using a column vectorcomprising x,y,z coordinates of voxel and the number 1. For example,p=(x,y,z,1). The matrix-vector product multiplies the vector column frommatrix M with the corresponding (x,y,z,1) values from column vector p.Summing the scalar-vector products generates the output vector p′: (x′,y′,z′,1). In such an example, the upper 3×4 elements of the matrix M,may contain real numbers used to store a combination of translations,rotations, scales, and/or shears (among other operations) that areapplied to p. The final row in this exemplar would be (0 0 0 1). Atransformation matrix of this form may be generated using one or more ofa variety of neuroimaging analysis software. The use of a labeleddataset with parcellations/segmentations as input to co-registration,specifically in the case of CT imaging, overcomes many of thelimitations of the prior state of the art (e.g., relating to intensityscaling differences or tissue boundary difference), and generatesreliably accurate co-registration even in presence of anatomical defects(e.g. brain stroke), brain lesions (e.g. tumors) or other imagingartifacts.

In block 110, the datasets generated in block 104 are aligned to eachother using the transformation matrix.

FIG. 2 shows a flow diagram for a method 200 for generation of surfacemodels of cortical and subcortical brain regions in accordance with thepresent disclosure. Though depicted sequentially as a matter ofconvenience, at least some of the actions shown can be performed in adifferent order and/or performed in parallel. Additionally, someimplementations may perform only some of the actions shown. Operationsof the method 200 may be performed by a computing system as disclosedherein on a labeled dataset as produced by the method 100.

The surface models generated by the method 200 include standardized meshmodels derived from a population-level atlas that allows for apoint-to-point correspondence between any point on one subject's surfacewith the same point on the surface in another subject.

In block 202, all voxels matching labeled values for the cortical orsubcortical region of interest are extracted into a new 3D datasetcontaining only those voxels of interest. For example, the new datasetmay include the right hippocampus as identified during the segmentationprocessing of the method 100.

In block 204, the 3D dataset of the segmented cortical or subcorticalregion of interest formed in block 202 is converted into a surface meshmodel using standard volume-to-surface conversion. In general, standardvolume-to-surface conversions may be achieved using existing open-sourceneuroimaging software. General and exemplary embodiments of such methodsinclude volume binarization followed by surface tessellation using thebinarized value to generate a mesh (e.g., as provided by Freesurfer:https://surfer.nmr.mgh.harvard.edu/fswiki/mri_tessellate) or a marchingcubes algorithm as made available by the open source Vascular ModelingToolKit software (VMTK:http://www.vmtk.org/vmtkscripts/vmtkmarchingcubes.html). In oneexemplary embodiment a surface/anatomical mesh model may be defined aspoints in 3D space that are joined by lines to form triangles, each ofwhich has a 2D face and which are combined according to a specificvolume geometry to form a topologically accurate representation of theobject modeled. The surface/anatomical mesh model may be, in oneexemplary embodiment, a 2D model (e.g., a plane) that is then folded in3D space to depict a 3D object (e.g., a brain surface).

In block 206, the curvature and sulcal features of the resulting surfacemodel are computed, which are then used to nonlinearly align an inflatedversion of the surface model to match a high-resolution atlas of thesame region generated from labeled population data. In this context, ahigh-resolution atlas may refer to a reference template pattern ofcortical curvature data that, in some exemplary embodiments, waspreviously prepared as an average pattern computed from a group ofrepresentative subjects and made available as part of the standardrepository. In other exemplary embodiments such template data may begenerated using selected subject populations (e.g., patients at aparticular institution operated on in a certain way).

In block 208, a standardized surface mesh that is already in alignmentwith the atlas, is overlaid on the subject's original surface model, andcoordinates of this standardized surface mesh are replaced by aresampling of the surrounding coordinates of the subject's nativecoordinate space. The surface meshes comprise many thousands of pointsin space called nodes, connected by lines to form triangles, whose facesform the mesh of the surface model. In the context of a standardizedsurface, the mesh model comprises a fixed number of nodes and maintainsa specific node-to-atlas region correspondence (i.e., each nodecorresponds to the same region in the atlas), and this correspondencemay be preserved across subjects. To preserve the correspondence, foreach subject, the standardized surface mesh and the subject's ownoriginal surface mesh are both deformed in a non-linear fashion to alignto a spherical template mesh derived from the aforementionedhigh-resolution population atlas. Both subject and standardized meshesare warped to maximize the overlap between sulcal and curvaturepatterns. Once both subject and standardized surface meshes are alignedto the template atlas, and are therefore in alignment with each other,the nodes of the standardized surface mesh are assigned an average ofthe coordinates of a subset of the surrounding nodes from the subject'soriginal surface mesh (an exemplary embodiment of which could be the 4nearest nodes). In this fashion, the standardized surface is warped tothe subject's anatomical coordinate space while both are aligned to thetemplate, thereby preserving the one-to-one correspondence of thestandardized surface with the template atlas. Once deflated from thespherical configuration used during co-registration, the standardizedsurface mesh assumes the topology of the subject's own anatomy, whilecontinuing to maintain its one-to-one correspondence between node andanatomical atlas identity. In this fashion, a surface-based comparisoncan be performed across subjects with high levels of accuracy simply bycomparing a specific node to the same node between surfaces.

The operations of the method 200 may be repeated for the contra-lateralhemispheric region, and for any other additional cortical or subcorticalor other labeled/segmented or parcellated brain surface to be generated.For information regarding generation of a standardized surface forcortical surfaces see, for example, Saad, Z. S., Reynolds, R. C., 2012.Suma. Neurolmage 62, 768-773.http://dx.doi.org/10.1016/j.neuroimage.2011.09.016; Kadipasaoglu C M,Baboyan V G, Conner C R, Chen G, Saad Z S, Tandon N. Surface-based mixedeffects multilevel analysis of grouped human electrocorticography.Neuroimage. 2014 Nov. 1; 101:215-24. doi:10.1016/j.neuroimage.2014.07.006. Epub 2014 Jul. 12. PMID: 25019677).However, no such method is known for generating standardizedsurface-based meshes of subcortical regions. Exemplary embodiments ofsuch regions may include the hippocampus, amygdala, thalamic nuclei andbasal ganglia. Such surface models could be used to create standardizedsubcortical surfaces for individual anatomy, allowing for concordance ofthese subcortical structures between individuals in a manner notpreviously done.

Use of the method 200 for brain regions not conventionally included insurface-based modeling or analyses (e.g. hippocampal and/or amygdalaand/or subcortical regions), in conjunction with the use ofhigh-resolution anatomical atlases of the regions to enable thegeneration of standardized surface meshes, is a significant improvementover the prior state of the art, which has previously constrained suchmethods to only cortical regions (e.g. strictly gray or white mattersurfaces).

FIG. 3 shows a flow diagram for a method 300 for automated segmentationof brain vasculature, and the generation of 2D/3D surface- andvolume-based models in accordance with the present disclosure. Thoughdepicted sequentially as a matter of convenience, at least some of theactions shown can be performed in a different order and/or performed inparallel. Additionally, some implementations may perform only some ofthe actions shown. Operations of the method 200 may be performed by acomputing system as disclosed herein.

In block 302, one or more imaging scans of a subject's brain areobtained. The imaging scans include a contrast-weighted MRI scan (e.g. aT1-weighted MRI with contrast, which will be referred to as the ContrastMRI dataset).

In block 304, the imaging scans are converted from original imagingstorage formats (e.g. DICOM) to 3D datasets as per the method 100.During conversion, the intensity values of the contrast MRI are variablyup-scaled (e.g. 100×) to facilitate the differentiation between contrastenhanced structures (e.g., blood vessels) from their surroundings.

In block 306, a sequence-adaptive multimodal segmentation algorithm isused to generate a labelled dataset from the Contrast MRI, as per themethod 100.

In block 308, the mask from the labeled dataset, generated as describedin the method 100, for the Contrast MRI dataset is used to sub-selectall voxels identified as belonging to the cerebrospinal fluid (CSF)region. The CSF mask (the sub-selected voxels) provides a novelimprovement to the blood vessel segmentation algorithm, as thehigh-intensity voxels representing blood vessels are localized mostcommonly in the CSF, adjacent to the pial surface. This is especiallytrue for those blood-vessels considered to confer the greatest risk forclinically significant hemorrhage (typically vessels with diameters 1.5mm. Similar masks for gray and white matter labeled regions are alsogenerated.

In block 310, a multi-scale filtering algorithm designed to enhance thetubular features in imaging data is used to extract blood vessels fromadjacent voxels reflecting CSF or background noise. In general, thefiltering algorithm utilizes a Hessian-based eigen-decomposition toderive eigen values and vectors at each pixel of the dataset at varyingspatial scales to select tubular structures corresponding to bloodvessels of differing diameters (see, for example: Frangi, Alejandro F.,et al. Multiscale vessel enhancement filtering. Medical Image Computingand Computer-Assisted Intervention—MICCAI'98. Springer BerlinHeidelberg, 1998. 130-137). The software algorithm returns an outputthat has assigned to each voxel a weight of “vesselness”, which range,for example, from 0 to 1, where higher weights representing voxels withmore vessel-like features (e.g. tubular).

In block 312, information from the vesselness-weighted dataset isintegrated with the Contrast MRI dataset to weight intensity value ofthe non-zero voxels by their relative “vesselness” weight and topenalize those voxels that overlap with white or gray matter.

In block 314, the blood vessel dataset is aligned to the Anatomical MRIdataset using the transformation matrix generated in block 306 (asdescribed in the method 100).

In block 316, the blood vessel dataset is converted into a surfaceanatomical mesh model (as described for block 204 of the method 200)that can be visualized using varying levels of transparency andillumination.

FIG. 4 shows a flow diagram for a method 400 for visualizing underlyingbrain structures in accordance with the present disclosure. Thoughdepicted sequentially as a matter of convenience, at least some of theactions shown can be performed in a different order and/or performed inparallel. Additionally, some implementations may perform only some ofthe actions shown. Operations of the method 400 may be performed by acomputing system as disclosed herein.

In block 402, a cutting plane (e.g., a 2D cutting plane) in 3D space, inany user defined axis, intersects with a 3D volume or a surface. At theintersection of the cutting plane with a given surface mesh model or the3D volume, all components of the mesh on either side of the cuttingplane can be rendered selectively visible, invisible, orsemi-transparent. On this plane any surface and/or volumetric(structural or functional) data can be simultaneously visualized. Ingeneral, the operations of block 402 may be performed by defining acutting plane along one and/or more of the 2D anatomical imaging planesof the imaging dataset (exemplary embodiments of which include thecoronal, sagittal, or axial plane of an MRI and/or CT scan). Theintersection of the cutting plane and the imaging dataset may be definedalong any arbitrary geometry (exemplary embodiments of which couldinclude orthogonal and/or oblique angles), and the points along theintersection of these two planes identifies the voxels of the associated3D volumetric imaging dataset that will be used for further displayand/or analysis. These voxels may then be selectively visualizedalongside the subject's 3D surface model. And the relationship of thecoordinates of the voxel to the coordinates of the 3D surface models(e.g. at their points of intersection) may be further used toselectively render components of the surface model visible, invisible,or semi-transparent on either side of the cutting plane, or along thecutting plane itself. The coordinates of the voxels along the cuttingplane may also be used to determine their distance from the implantedelectrodes in the subject, which may then be used to compute andgenerate surface and/or volumetric data representations in relation tothe cutting plane.

In block 402, when applied to extra- and intracranial anatomical meshesas defined in the methods 200 and/or 300, qualitative and quantitativeanalyses of the surface or volume intersected by the cutting plane maybe computed, including but not restricted to computation of morphometricfeatures such as curvatures, thickness, and area of cortical gray/whitematter and/or subcortical structures, as well as the hippocampal andamygdala curvatures, thickness and areas. Further, the edges of theintersected surface and/or volume can be selectively raised or loweredto increase precision of visualization. In one exemplary embodiment, theintersection of the cutting plane with specific elements of the 3Dsurface may include a variety of cortical and/or subcortical surfaceslayers, exemplary embodiments of which include the pial and/or whitematter surfaces. For these exemplary embodiments, the intersection ofthe cutting plane with these surfaces will determine intervening graymatter between these two exemplary surfaces. The intervening gray mattermay otherwise be referred to as the cortical ribbon. And by computingthe distances between the points of the pial and white matter surfacesthat lie along their intersection with the cutting plane (e.g.,orthogonal distances between these two surfaces at their intersectionwith the cutting plane), the thickness of the cortical ribbon can becomputed. By integrating the thickness across the length of the corticalribbon, the area can be computed. In another exemplary embodiment, thecurvature of the surfaces (e.g. the pial surface mesh) may be computedby drawing orthogonal lines outward from the faces of the surface meshtriangles to determine if these lines intersect the face of another meshtriangle. Such intersections occur when two triangular faces are pointedtowards each other, as in the case of sulci. Using the angle anddistance of such intersections, local topological features such assurface curvature and sulcal boundaries may then be determined.

In block 404, when applied to the extra- and intracranial anatomicalmeshes as defined in the methods 200 and/or 300, the visualization ofbrain structural data (including one or more of MRI, CT, PET, fMRI, DTI)and/or brain activity data (including one or more of EEG or MEG or brainstimulation) in relation to the cut planes of the various anatomicalmesh models may be selectively depicted.

In block 406, when applied to the extra- and intracranial anatomicalmeshes as defined in the methods 200 and/or 300, users can make virtualcuts of arbitrary shape or geometry (e.g. a dome shaped surface or asurface that matches a craniotomy) by selecting a path along a surfaceand how deep within the surface to apply the cut, in which fashion auser can model and visualize surgical approaches or various anatomicalboundaries for clinical evaluation and/or surgical planning or trainingand/or educational visualizations.

FIG. 5 shows a flow diagram for a method 500 for automated planning ofelectrode or probe implantation in accordance with the presentdisclosure. Though depicted sequentially as a matter of convenience, atleast some of the actions shown can be performed in a different orderand/or performed in parallel. Additionally, some implementations mayperform only some of the actions shown. Operations of the method 500 maybe performed by a computing system as disclosed herein.

The method 500 provides for precise and automated planning of electrodeor penetrating probe implantation trajectories using a prior probabilitydistribution derived from a previously implanted population to theanatomical target to be implanted or targeted. The prior probabilitydistribution is generated using the entry and/or target coordinates ofthe trajectories from the previously implanted population, which havebeen aligned to a subject's brain using linear or non-linear deformationalgorithms (or vice-versa). Further, the general implantation strategyis derived from clinical consideration of the likely anatomical region/susing a description of the clinico-electrical syndrome linking thesubject's seizure semiology or other electro-clinical characterizationsof epilepsy. In each case, the trajectory will have the additional goalof avoidance of critical structures (e.g. blood vessels) through the useof loss-functions and risk-metrics. Additionally, these trajectoriesmay, in one embodiment, be solely created by a physician or surgeon,skilled in the art of stereotaxis, by defining entry and target pointsof interest.

In block 502, one or more imaging scans of a subject's brain areobtained. The imaging scans include a target anatomical MRI scan (e.g.,a T1-weighted MRI without contrast) and a contrast-weighted scan (e.g.T1-weighted MRI with contrast).

In block 504, the imaging scans are converted from original imagingstorage formats (e.g. DICOM) to datasets as per the method 100. TheT1-weighted MRI without contrast is converted to a dataset referred toas the Anatomical MRI dataset, and the T1-weighted MRI with contrast isconverted to a dataset referred to as the Contrast MRI dataset.

In block 506, a blood vessel dataset and mesh model are generated andco-registered to the Anatomical MRI dataset and its related mesh modelsas per the method 100.

In block 508, a predicted entry and target point coordinate in thecurrent subject's own anatomical space is defined using target point andentry point coordinates curated from the population cohort of priorimplanted subjects. Coordinates from the population were previouslyco-registered to a high-resolution template atlas. In one exemplaryembodiment the template coordinate space could be the coordinates spacedefined as in standard co-ordinate space (e.g. Talairach space; MontrealNeurological Institute space). Co-registrations may be achieved usingnon-linear or linear/rigid/affine transformations, and computed in aninverse-consistent and symmetric fashion such that the templatecoordinates can be transformed to the patient's coordinate space in atopologically accurate fashion and the reverse transform can be appliedfollowing implantation of the current subject's electrodes, to furtheradd to the population prior dataset.

In block 510, for each probe, the group of entry and target pointcoordinates for that probe are averaged to generate a mean target andentry point in subject's anatomical coordinate system, from which a meantrajectory is defined. For each probe, anatomical parcellations arefurther associated with the probe and can be used to further constrainthe predicted trajectory in exemplary cases where the anatomical regionof interest is small in volume, and/or in close proximity to othersensitive anatomical structures, and/or the variability in targetcoordinate spread from the prior population may be larger than thediameter of the structure. In this fashion, in one exemplary embodiment,anatomical parcellations and prior implanted trajectory distributionscan be used to develop prior information to enable the implantation ofthe penetrating probe. In another exemplary embodiment, the anatomicaltarget may be quite large (e.g. as in the cingulate gyrus, which extendsin a C-shape from the anterior portions to the posterior aspects of thecranium) and in such circumstances, the target coordinates from theprior population may constrain the desired target location to theanterior, middle, or posterior aspect of the cingulum (exemplartrajectories here would be AC=anterior cingulate; MC=middle cingulate;PC=posterior cingulate), while the anatomical parcellations may furtherconstrain the final target coordinate to remain within the boundaries ofthe cingulum, which is known to curve from inferior to superior and backdown, as well as from anterior to posterior and back, along its route.In a further exemplary embodiment, the patient's seizure semiology mightbe used by a clinician who is skilled in the art to derive informationabout the likely specific anatomical region responsible for theepilepsy. This understanding can then be translated to inform thetrajectory plan by constraining the trajectory to the anatomicalparcellation of interest. In another embodiment, surgical trajectoriesmay solely be manually created by an individual skilled in the art ofstereotaxis, by defining entry and target points of interest, andoptimizing these relative to blood vessels and other possibletrajectories. Alternatively, they may be derived by some combination ofderivation from the mean population-based trajectory combined withmanual optimization.

In block 512, constraint to anatomical parcellations may be achieved byadjusting the trajectory so that it intersects with the nearest voxelwith the label assigned from the anatomical region of interest, wheredistance is determined by computing Euclidean distance between thevoxels of the probe trajectory and the labelled voxels in the anatomicalparcellation of interest.

In block 514, the intersection of the trajectory with any criticalstructure, such as blood vessels is evaluated using a loss-function, bydetermining such trajectory intersections with 2D and/or 3D surfaceand/or volume regions labelled or identified as belonging to thecritical structure, with penalization of trajectories for any suchintersections. Further, the proximity of this trajectory to a criticalstructure such as a blood vessel is checked against user-determinedconstraints (e.g. >2 mm from the edge of blood vessel, or ≥4 mm from thecenter of an adjacent probe's trajectory).

Automated loss or optimization functions may also be incorporated in thetrajectory planning, such that—in one exemplary embodiment—the totalintracranial length is minimized while gray matter sampled is maximizedto enable maximal potential of recording.

In block 516, beginning from initial trajectory estimated by the priorinformation (defined by the mean entry and target points across thepopulation), the method 500 commences a local search surrounding thismean point until a trajectory as close as possible to the meantrajectory, that satisfies all safety and optimization criteria isidentified. The search region is defined as a frustum with the diameterof each end defined in terms of standard deviation of the distributionof target and the center of each end defined by the mean entry andtarget coordinates from the group population.

In block 518, the final trajectories are superimposed on the AnatomicalMRI dataset to generate a new planning dataset that can be exported inany fashion for use with other software or hardware systems to visualizethe trajectory plans in relation to the subject's anatomy.

When used with the visualization techniques of the method 400,verification of an implantation plan of multiple stereotactic depthprobes along oblique trajectories is aided by simultaneous visualizationof a cortical mesh model of surface topology and deep anatomy revealedby structural magnetic resonance imaging, sliced along a plane colinearwith the proposed trajectory. The slicing of the brain surface in anygiven plane enables the visualization of deep buried cortex in 3D andalso enables rapid confirmation of the surgical plan by clinicians.

Integration of the planning operations of the method 500, with theanatomical visualization and analysis techniques of the methods 100-400,enable a clinician to identify critical extra cranial and intracranialstructures (including but not limited to structures such as ventricles,white matter pathways, vessels and eloquent regions) and avoid unwantediatrogenic outcomes that include but not limited to hemorrhage and/orvisual, linguistic, cognitive, spatiotemporal, and/or sensorimotordeficits.

Incorporating the methods 100-500 with white-matter pathway analysesusing deterministic or probabilistic tractography (derived fromdiffusion imaging) can further identify risks to pathways involved incrucial functions such as motor, sensory, auditory, or visual processes.In a specific indication—this approach may be applied to the reductionof visual deficits after laser interstitial thermal therapy of thehippocampus and/or amygdala for mesial temporal lobe epilepsy. 3Dplanning of the optimal trajectory for targeting the medial temporallobe, combined with the visualization of pathways identified bydiffusion imaging may be used to increase the therapeutic window of thistechnique.

FIG. 6 shows a flow diagram for a method 600 for automated localization,naming, and visualization of previously implanted electrodes orpenetrating brain probes, combined with resolution of implantedstructures in post-surgical imaging using a template-matching searchalgorithm and planned trajectories. Though depicted sequentially as amatter of convenience, at least some of the actions shown can beperformed in a different order and/or performed in parallel.Additionally, some implementations may perform only some of the actionsshown. Operations of the method 600 may be performed by a computingsystem as disclosed herein.

In block 602, one or more imaging scans of a subject's brain areobtained. The imaging scans include a target anatomical imaging scan(e.g. T1-weighted MRI without contrast) and a post-implantation CTimaging scan obtained after electrodes are implanted, to be used tolocalize each electrode's actual location.

In block 604, the imaging scans are converted from original imagingstorage formats (e.g. DICOM) to datasets as per the method 100. TheT1-weighted MRI without contrast is converted to a dataset referred toas the Anatomical MRI dataset, and the CT imaging scan is converted to adataset referred to as the CT electrode dataset.

In block 606, both datasets are co-registered, and the CT Electrodedataset is aligned to the Anatomical MRI as per the method 100.

In block 608, a third imaging scan is obtained. The third imaging scanis actually used by the surgeon as the anatomical imaging dataset duringsurgery to guide electrode implantation (referred to herein as theImplant MRI dataset or imaging scan). In one exemplary embodiment, thisscan may be a T1-weighted MRI with contrast enhancement, used to provideboth high resolution anatomical detail and to reveal location of bloodvessels during implantation planning. The Implant MRI imaging scan isimported, co-registered, and aligned to the Anatomical MRI as per themethod 100.

In block 610, a trajectory implant data file (referred to herein as theimplant log) is obtained. The implant log was created when theimplantation was performed, an exemplary embodiment of which is asubject's implantation file generated by a robotic sEEG implantationsystem (e.g. the Zimmer ROSA™ robot), and another example is thestereotactic file created by navigation systems (e.g., BrainLab™ andMedtronic Stealth™). The implant log includes information regardingprobe/trajectory names and/or the planned target and/or entry pointcoordinates and/or the probe trajectory vector defined in relation tothe patient's coordinate space of the Implant MRI (as described in themethod 500).

The method 500 may also include a security/manual verification featurewhereby the user is manually required to enter the probe names, numberof electrodes on each probe, and the number of electrodes to ignore fromeach probe (e.g. for electrodes in the anchor bolt, outside of thebrain, or not included in the recording, etc.). The initial list ofnames and estimated electrodes per probe may be automatically obtainedand provided to the user as a template by reading the aforementionedinformation directly from the implant log or its equivalent should it beavailable.

In block 612, the planned target and entry point coordinates providedfor each probe from the implant log, along with numbers of electrodes ineach probe as verified by the user, are used to compute an initial listof expected coordinates for each electrode. This computation isperformed using the axis of the trajectory computed from the entry andtarget point coordinates, the distance between the entry and targetpoint coordinates, and the spacing between electrodes. This informationis used to generate “dummy” objects at the estimated locations of eachelectrode for each of the probe trajectories. An exemplary embodiment ofsuch a “dummy” object may be a sphere that is centered around thecoordinate with a given geometry that matches the electrode geometry(e.g. a cylinder). This new dataset (referred to herein as the plannedtrajectory dataset) has the same volume geometry and coordinate space asthe implant MRI dataset.

In block 614, the planned trajectory dataset is aligned to theAnatomical MRI dataset using the transformation matrix generated by thealignment of the Implant MRI dataset to the Anatomical MRI dataset.

In block 616, Using the CT Electrode and Planned Trajectory datasets areco-registered to the subject's Anatomical MRI dataset using as per themethod 100, and a binarization operation is performed on the CTElectrode dataset in which imaging voxels (e.g. possibly of 1 mm×1 mm×1mm cube dimensions containing intensity information from the image,functioning as a 3D pixel equivalent) below an automatically determinedthreshold level are zeroed out. A 3D clustering algorithm is applied tothe remaining voxels to identify voxels with high intensity signal(sometimes referred to as a metallic artifact) of the electrode contactson the CT scan. An exemplary embodiment of the 3D clustering algorithmcould be the standard clustering commands provided by the underlyingneuroimaging analysis software used. An iterative search is performed byadjusting threshold value until the resulting numbers of clusters aresimilar to the expected number of electrodes. The coordinates of theseclusters are iteratively compared to the coordinates of the spherical“dummy” electrodes generated for the Planned Trajectory datasets. Usingline-distance in 3D space and center of mass for distance metrics, theclusters from the CT Electrode dataset and the trajectory paths andsphere object coordinates from the Planned Trajectory datasets areiteratively searched and optimized until all expected electrodes areidentified and localized.

In block 618, the clustering algorithm combined with the trajectory pathinformation and the number of electrodes expected, as provided by theinput log and represented in the planned trajectory dataset, are used toadjust the final locations of the electrode coordinates. Finalcoordinate locations that best match the imaging data (e.g. clusterlocation), and the physical constraints of the expected trajectory(location along a specific line separated by a specific distance fromadjacent electrodes on the same path) are thus derived.

In block 620, after all electrode coordinates are identified, 2D and/or3D models of these electrodes are rendered for visualization, withappropriate electrode names and numbering schemes assigned. Electrodesare visualized using displayable objects, (e.g. cylinders or discs),that reflect the size, spacing and dimensions of each actual electrode.Having been co-registered to the anatomy MRI dataset, these electrodescan be visualized in relation to the 2D and/or 3D surface- andvolume-based representations of the relevant extracranial andintracranial structures generated by the methods 200 and/or 300.

Displayable electrode objects may be individually manipulated (e.g.colored, annotated, numbered, visualized using different shapes orrepresentations, turned on or off). They can be rendered transparent,translucent, or invisible along with any functional data (EEG) collectedby the electrodes.

The methods and techniques described herein can be used in conjunctionwith other methods for the surface-based representation of recordedintracranial EEG or other general functional activation or generalneural correlate measured using implanted electrodes and/or penetratingprobes and/or imaging modalities. The method disclosed herein forsurface-based representations can be applied not only to display therepresentation upon cortical structures but also the anatomical meshesgenerated for the hippocampus, amygdala, and/or other generalsubcortical or brain structures. Such methods are disclosed in U.S. Pat.No. 10,149,618.

Using the methods disclosed herein, data representations of interest canbe constrained to specific electrodes and exported to a new dataset bysuperimposing the intensity values of the voxels of interest upon theAnatomical MRI dataset to generate a new Surface- or Volume-Activationdataset. In surface-based datasets, the activations are assigned tosurface-nodes using geodesic spread functions as described in priorpublications (Kadipasaoglu C M, Baboyan V G, Conner C R, Chen G, Saad ZS, Tandon N. Surface-based mixed effects multilevel analysis of groupedhuman electrocorticography. Neuroimage. 2014 Nov. 1; 101:215-24. doi:10.1016/j.neuroimage.2014.07.006. Epub 2014 Jul. 12. PMID: 25019677). Involume-based datasets, the activations are constrained to the voxelsthat are located within the boundary between the pial and white mattersurface layers (the cortical ribbon) underlying the electrodes ofinterest (Christopher R. Conner, Gang Chen, Thomas A. Pieters, NitinTandon, Category Specific Spatial Dissociations of Parallel ProcessesUnderlying Visual Naming, Cerebral Cortex, Volume 24, Issue 10, October2014, Pages 2741-2750, https://doi.org/10.1093/cercor/bht130). Thesedatasets can be exported to disc in any fashion for use with othersoftware or hardware systems to visualize these activations in relationto the subject's anatomy.

FIG. 7 shows a pictorial representation of co-registration of differentneuroimaging modalities performed on a single subject in accordance withthe present disclosure. In FIG. 7, sequence adaptive segmentation isapplied to the dataset 702 to produce the labeled dataset 706, andsequence-adaptive segmentation is applied to the dataset 704 to producethe labeled dataset 708. The labeled datasets 706 and 708 areco-registered and a transformation matrix produced by theco-registration is applied to align the labeled datasets 706 and 708 asshown in dataset 710.

FIG. 8 shows a pictorial representation depicting a generation of 2D/3Dsurface models of the hippocampus as well as the thalamus in accordancewith the present disclosure. FIG. 8A is an exemplary illustration of ananatomical mesh model of the right hippocampus generated from thesegmentation of a 3D volumetric dataset of a subjects anatomical T1 MRIshown in FIG. 8B.

FIGS. 8B and 8C depict exemplary illustrations of 2D/3D surface meshmodels of the left hippocamps and amygdala in a subject, which arevisualized concurrently with the same subject's right cerebralhemisphere following an anatomical atlas-based parcellation. In FIG. 8C,the surface models are visualized as distinct structures. The leftcortical hemisphere has been rendered transparent independently of theright, to allow for visualization of the left hippocampus and amygdala.In FIG. 8D, the parcellated right cortical hemisphere is renderedsemi-transparent such that the solid-state rendering of the underlyingright hippocampus and amygdala can be visualized.

FIGS. 8E and 8F illustrate an exemplary surface-based mesh model of asubject's left thalamus and its nuclei generated using parcellationsderived from a microscopic stereotactic atlas. In FIG. 8E, the surfacemodel is view in isolation. In FIG. 8F, the same model is viewed inrelation to the three principle planes of the subject's originalanatomical T1 MRI.

FIGS. 9A-9D show example steps for segmentation of humancerebrovasculature in accordance with the present disclosure. FIGS.9A-9C depict the original imaging dataset (FIG. 9A, an exemplaryembodiment of which here is a T1 MRI with contrast) that is subsequentlymasked by the cerebrospinal fluid segmentation volume (FIG. 9B) and thenprocessed using a multi-scale Hessian based filtering algorithm toaccurately segment the blood vessel voxels (FIG. 9C). FIG. 9D depictsthe resulting vascular 3D surface model generated using the segmentedblood vessel volume (right) as well as the overlay of the surfacecerebrovascular model (outlined) over the three principal planes of theoriginal contrasted T1 MRI dataset, demonstrating a comprehensivesegmentation of the subject's vasculature.

FIGS. 10A-10N and 10P-10W show a pictorial representation of using a 2Dcutting plane (“slicer”) to intersect with 2D and/or 3D surface andvolume models at arbitrary angles to optimize the visualization ofcortical and subcortical structural and/or functional representations.FIGS. 10A-10C depict a cutting plane viewed on a 2D sagittal plane viewof a subject's anatomical T1-weighted MRI overlaid with CT skull shownin FIG. 10A. The 3D surface model of the subject's intact skull is shownin FIG. 10B, and the skull following application of the cutting plane isshown in FIG. 10C. The skull is rendered partially transparent tovisualize the underlying parcellated cortical surface model with thesame cutting plane applied.

FIGS. 10D-10F show rotated views of the same subject skull andunderlying parcellated cortical surface model. Note that the cuttingplane may be rendered opaque and constricted within the boundaries ofthe 3D surface models, to display the associated 2D MRI planar images(FIG. 10D). Alternatively, the cutting plane may be renderedsemi-transparent and/or extend the 2D MRI plane views beyond theboundaries of the underlying surface models (FIG. 10E). Finally, thecutting plane may be rendered invisible, and the underlying plane of thesurface model rendered transparent, such that deep anatomical structuresmay be visualized (FIG. 10F).

FIGS. 10G, 10H, and 10I show sagittal views of the 2D cutting plane andassociated 3D parcellated cortical surface model at various rotatedangles. In FIG. 101, the edges of the cortical model are extendedslightly beyond the boundaries of the cutting plane, with gyral andsulcal boundaries selectively enhanced to more precisely visualizeunderlying anatomical features.

FIGS. 10J-10L show three views of the same 2D sagittal cutting plane and3D cortical surface model shown in FIGS. 10G-101, with the edges of themodel retracted from the cutting plane (FIG. 10J), flush with the plane(FIG. 10K), and extended slightly beyond the plane (FIG. 10L).

FIGS. 10M, 10N, and 10P show three views of a 2D coronal cutting planeand the 3D skin and parcellated cortical surface model. The intact skinmodel is intersected by the cutting plane, and the remaining componentof the skin and parcellated cortical model are visualized (FIG. 10M).The parcellated cortical model is shown in isolation in FIG. 10N and inreference to the cutting plane, with the edges slightly extended beyondthe plane, and then again in a third view, but in this view the edgesare constrained to just the gray matter and white matter boundaries,such that the extension of the gray and white matter boundary edgesbeyond the cutting plane will isolate the intervening cortical ribbon.In FIG. 10P, an enlarged and slightly rotated view of the third imagefrom FIG. 10N is depicted, with a white arrow indicating an exemplaryregion of the aforementioned cortical ribbon contained between the edgesof the gray and white matter boundaries (FIG. 10P).

FIGS. 10Q-10W show cortex with simultaneous representation of surfaceand deep anatomical structures along with cortical activity representedas a color scale, via a slice along a plane colinear with a depthtrajectory. The visualization of brain structural data (including one ormore of MRI, CT, PET, fMRI, DTI) and/or brain activity data (includingone or more of EEG or MEG or brain stimulation) in relation to the cutplanes of the various anatomical mesh models may be selectively depictedto optimize visualization of functional activation in neocortical (FIGS.10Q, 10R, 10S, 10T, and 10U) and/or hippocampal and amygdala (FIGS. 10Vand 10W) and/or subcortical or other brain regions. Cutting planes andthe associated viewpoint from which the relevant surfaces are visualized(FIGS. 10T-10 y) are depicted by the line 1002 and arrow 1004,respectively

FIGS. 11A-11H, 11J-11N, and 11P-11R show a pictorial representation ofpopulation-derived anatomical targeting for electrode or penetratingprobe implantation, which incorporate priors derived using probabilitydistributions from previously implanted populations and/or anatomicalatlas-based parcellations and segmentations. FIGS. 11A-11D. depicts agrouped representation of trajectories into the brain from 130 patientsimplanted with 2600 electrodes to probe epilepsy, which have beenco-registered and aligned to a common brain space and color coded byentry and target points (FIG. 11A). Electrodes may be furthercolor-coded based on standard regional nomenclature applied to them,indicating similar entry and target points for specific cortical orsubcortical foci across individuals, an exemplary embodiment of which isdepicted for the right amygdala and hippocampus in a single subject(FIG. 11B). Using the information of prior trajectories from thispopulation, a new trajectory may be derived for any specific brainregion for a new individual (not one of the prior 130). An exemplaryillustration of the analysis is provided for a single subject's rightanterior hippocampus (RAH), where the new trajectory is depicted as theelongated cylinder, while the population prior trajectories are depictedeither using each individual probe (FIG. 11C, shorter cylinders) or byvisualizing the mean and variance of this population (FIG. 11D), whichis depicted in this exemplary illustration with a frusta using the meanand 1.5× the standard deviation of entry and target point coordinates.

FIG. 11E depicts the integration of the oblique cutting plane, thedetailed cerebrovascular and parcellated anatomical mesh models, and thetrajectory planning algorithm to generate automated implantationtrajectory plans for 12 different brain probes (e.g. sEEG probes). Theautomated algorithm ensures adherence to multiple safety constraints,exemplary embodiments of which may be a minimum distance from adjacentvessels along the trajectory as well from adjacent probes. The panel11E-2 depicts an exemplary illustration of manual trajectoryoptimization in which two cylinders are visualized, representing theoriginal (I.e. automatically-derived) and manually-adjusted trajectoriesfor a right anterior hippocampal (RAH) probe.

FIG. 11F depicts a similar population-level derived plan forlaser-interstitial thermal therapy of the amygdala and/or hippocampusfor mesial temporal lobe epilepsy. Visualized are optimal newtrajectories for a new subject, with the population-derived predictedablation volumes expected for the given trajectories.

FIGS. 11G and 11H depict exemplary illustrations of new trajectoriesderived from the population data of prior implanted trajectories formultiple regions in the left cingulate gyrus, including the left rostralcingulate (LRC), anterior cingulate (LAC), medial cingulate (LMC) andposterior cingulate (LPC) regions. These illustrations include a lateralview highlighting the entry points (FIG. 11G) and a medial viewdepicting the proposed trajectories labeled by the aforementioned targetbrain regions, in which the left hemisphere has been rendered fullytransparent such that the cingulate gyrus of the right hemisphere isvisible (and may be used as a visual reference for the contra-lateraltarget brain regions), and in which the proposed trajectories have beendepicted with their associated frusta (derived from the population)rendered as a semi-transparent overlay (FIG. 11H).

FIGS. 11J-11N and 11P depict exemplary illustrations of another subsetof exemplary proposed trajectories that may be desired for a subjectundergoing stereoencephalography evaluation for refractory epilepsy,using both superior (FIGS. 11J-11L) and lateral views of the 3D corticalsurface model (11M-11N and 11P), in which the left hemisphere isrendered opaque (11J and 11M) or fully transparent (11K-11L, 11N, and11P). The middle illustrations depict the population data of priorimplanted trajectories used for the generation of their respective newtrajectory, using cylinders color-coded by target brain region tovisualize each of the prior implanted probes overlaid together withtheir respective new trajectory (FIGS. 11K and 11N). The mean and 1.5×the standard deviation from the distribution of coordinates of thepopulation of prior implanted trajectories are used to generate theaforementioned frusta, which are depicted as semi-transparent overlayswith their respective trajectories in the right-most illustrations (11Land 11P). The bottom row depicts an exemplary summary illustration ofall trajectories from the population data of prior implantations, whichare visualized with their respective frusta on a single exemplarysubject's 3D cortical surface model that has been rendered both opaqueand completely transparent (11Q and 11R, respectively).

FIG. 12A-12E depicts a pictorial representation of automated electrodelocalization and labelling, which incorporates the implantationtrajectory log from a robotic sEEG implantation system to constrain andinform the electrode search algorithm and provide probe names andnumbers of associated electrodes. The initial clustering algorithmapplied to the post-implantation CT Electrode dataset is depicted in12A, demonstrating how increasing intensity thresholds to zero outvoxels with intensities below the threshold may be used to identifyclusters of high-intensity voxels representing artifact from electrodecontacts in the CT scanner. The trajectory implantation log from therobotic implantation system may also be used to further inform theelectrode search by constraining the search space of the algorithm tomore efficiently separate signal relating to electrode artifact fromnoise (FIG. 12B), and also ensure final electrode coordinates are spacedand aligned in a fashion consistent with the actual implantation asdefined by the spherical dummy electrodes (FIG. 12C).

FIG. 12D depicts a cutting plane applied at an oblique angle to asubject's skull model to visualize the implanted electrodes in relationto the subjects right hippocampal and amygdala surface models. In thisexemplary embodiment, each electrode is rendered as a cylinder withinter-electrode spacing and dimensions dictated by the implantationtrajectory log and physical dimensions of the actual electrode. Probesand their respective electrodes are color-coded by probe name. A morezoomed view of the same subject's right hippocampus and amygdala isdepicted in FIG. 12E with a subset of the implanted probes visualized asdisplayable objects and color-coded by their probe name, which is alsoannotated in white. The trajectories from the trajectory implantationlog are also depicted here, though this time as semi-transparentcylinders with smaller dimensions and spacings to differentiatethemselves from the true electrode locations. As can be seen by thehighlighted electrode with the overlying crosshair, the final electrodecoordinates do not always perfectly correspond to the plannedtrajectory, as the probes may be deflected during implantation. Thehighlighted electrode coordinate corresponds to the coordinates of thecrosshairs in the adjacent 2D coronal and sagittal planar images of thesame subject's pre-implantation MRI overlaid by the post-implantationCT.

FIG. 13 shows a block diagram for a computing system 1300 suitable forimplementation of the methods disclosed herein (e.g., the methods 100,200, 300, 400, 500, and/or 600. The computing system 1300 includes oneor more computing nodes 1302 and secondary storage 1316 that arecommunicatively coupled (e.g., via the network 1318). One or more of thecomputing nodes 1302 and associated secondary storage 1316 may beapplied to perform the operations of the methods described herein.

Each computing node 1302 includes one or more processors 1304 coupled tomemory 1306, a network interface 1312, and the I/O devices 1314. Invarious embodiments, a computing node 1302 may be a uniprocessor systemincluding one processor 1304, or a multiprocessor system includingseveral processors 1304 (e.g., two, four, eight, or another suitablenumber). Processors 1304 may be any suitable processor capable ofexecuting instructions. For example, in various embodiments, processors1304 may be general-purpose or embedded microprocessors, graphicsprocessing units (GPUs), or digital signal processors (DSPs)implementing any of a variety of instruction set architectures (ISAs).In multiprocessor systems, each of the processors 1304 may commonly, butnot necessarily, implement the same ISA.

The memory 1306 may include a non-transitory, computer-readable storagemedium configured to store program instructions 1308 and/or data 1310accessible by processor(s) 1304. The memory 1306 may be implementedusing any suitable memory technology, such as static random-accessmemory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-typememory, or any other type of memory. Program instructions 1308 and data1310 implementing the functionality disclosed herein are stored withinmemory 1306. For example, instructions 1308 may include instructionsthat when executed by processor(s) 1304 implement one or more of themethods disclosed herein.

Secondary storage 1316 may include volatile or non-volatile storage andstorage devices for storing information such as program instructionsand/or data as described herein for implementing the methods describedherein. The secondary storage 1316 may include various types ofcomputer-readable media accessible by the computing node 1302 via thenetwork interface 1312. A computer-readable medium may include storagemedia or memory media such as semiconductor storage, magnetic or opticalmedia, e.g., disk or CD/DVD-ROM, or other storage technologies.

The network interface 1312 includes circuitry configured to allow datato be exchanged between the computing node 1302 and/or other devicescoupled to the network 1318. For example, the network interface 1312 maybe configured to allow data to be exchanged between a first instance ofthe computing system 1300 and a second instance of the computing system1300. The network interface 1312 may support communication via wired orwireless data networks.

The I/O devices 1314 allow the computing node 1302 to communicate withvarious input/output devices such as one or more display terminals,keyboards, keypads, touchpads, scanning devices, voice or opticalrecognition devices, or any other devices suitable for entering orretrieving data by one or more computing nodes 1302. Multipleinput/output devices may be present in a computing system 1300.

The computing system 1300 is merely illustrative and is not intended tolimit the scope of embodiments. In particular, the computing system 1300may include any combination of hardware or software that can perform thefunctions disclosed herein. Computing node 1302 may also be connected toother devices that are not illustrated, in some embodiments. Inaddition, the functionality provided by the illustrated components mayin some embodiments be combined in fewer components or distributed inadditional components. Similarly, in some embodiments the functionalityof some of the illustrated components may not be provided and/or otheradditional functionality may be available.

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present invention. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

What is claimed is:
 1. A method, comprising: obtaining a first imagingscan and a second imaging scan of a single subject brain; converting thefirst imaging scan to a first dataset, and the second imaging scan to asecond dataset; applying a sequence-adaptive multimodal segmentationalgorithm to the first dataset and the second dataset, wherein thesequence-adaptive multimodal segmentation algorithm performs automaticintensity-based tissue classification to generate a first labelleddataset and a second labeled dataset; automatically co-registering thefirst labeled dataset and the second labeled dataset to each other togenerate a transformation matrix based on the first labeled dataset andthe second labeled dataset; and applying the transformation matrix toalign the first dataset and the second dataset.
 2. The method of claim1, wherein the first imaging scan and the second imaging scan areperformed with one or more of magnetic resonance imaging (MRI),computerized tomography (CT), magnetoencephalography (MEG), or positronemission tomography (PET).
 3. The method of claim 1, wherein thesequence-adaptive multimodal segmentation algorithm assigns a numericlabel value to each voxel of the first dataset or the second dataset. 4.The method of claim 1, further comprising: extracting voxels from thefirst dataset having a label corresponding to a subcortical region ofinterest; forming a third dataset containing the voxels extracted fromthe first dataset; converting the third dataset into a first subcorticalsurface mesh model; computing curvature and sulcal features of the firstsubcortical surface mesh model; aligning the first subcortical surfacemesh model to a subcortical atlas of the region of interest using thecurvature and sulcal features; and overlaying the first subcorticalsurface mesh model aligned to an atlas of the subcortical region ofinterest on a second subcortical surface mesh model, the secondsubcortical surface mesh model having a standardized number of nodesthat enables a one-to-one correspondence between node identity and atlaslocation; and assigning coordinates of nodes of the firstsubcortical_surface mesh model to the second subcortical structuresurface mesh model such that the second subcortical_surface mesh modelassumes a topology of the first subcortical structure surface meshmodel.
 5. The method of claim 1, wherein: the first imaging scan is acontrast weighted MRI scan and the first dataset is a contrast weighteddataset; and the method further comprises: selecting, based on thelabeled dataset, voxels of the first dataset identified as belonging toa cerebrospinal fluid region; applying a multi-scale tubular filteringalgorithm to identify voxels of the first dataset representing bloodvessels and assign a vesselness weight value to each voxel; integratingthe vesselness weight values into the first dataset; and after aligningthe first dataset and the second dataset, converting the first datasetto a surface anatomical mesh model.
 6. The method of claim 1, wherein:the first imaging scan is a contrast weighted MRI scan and the secondimaging scan is an anatomical MRI scan; and the method furthercomprises: defining predicted target point coordinates and entry pointcoordinates for a probe based on target point coordinates and entrypoint coordinates of previously implanted probes or by user definedtarget and entry points; defining a trajectory for the probe based on amean target coordinates and mean entry point coordinates; adjusting thetrajectory to intersect with a nearest voxel assigned a label of ananatomical region of interest; checking proximity of the trajectory tocritical structures based on user defined constraints and/or userdefined modification of the trajectory to satisfy the user definedconstraints; and superimposing the trajectory on the second data set toform a planning dataset.
 7. The method of claim 1, wherein: the firstimaging scan is an anatomical MRI scan, the second imaging scan is apost-implantation CT imaging scan, the first dataset is an anatomicalMRI dataset, and the second dataset is a post-implantation CT imagingdataset; and the method further comprises: obtaining a third imagingscan used to guide electrode implantation during surgery; converting thethird imaging scan to a third dataset; aligning a third dataset with thefirst dataset; obtaining a trajectory implant data file created duringthe electrode implantation; generating a planned trajectory dataset,based on trajectory implant data file, that includes dummy objectsdisposed at locations of electrode geometry; aligning the plannedtrajectory dataset to the CT imaging dataset; and automaticallyidentifying and labelling electrodes in the CT electrode dataset basedon the dummy objects of the trajectory implant data file.
 8. Anon-transitory computer-readable medium encoded with instructions thatare executable by one or more processors to: obtain a first imaging scanand a second imaging scan of a single subject brain; convert the firstimaging scan to a first dataset, and the second imaging scan to a seconddataset; apply a sequence-adaptive multimodal segmentation algorithm tothe first dataset and the second dataset, wherein the sequence-adaptivemultimodal segmentation algorithm performs automatic intensity-basedtissue classification to generate a first labelled dataset and a secondlabeled dataset; automatically co-register the first labeled dataset andthe second labeled dataset to each other to generate a transformationmatrix based on the first labeled dataset and the second labeleddataset; and apply the transformation matrix to align the first datasetand the second dataset.
 9. The non-transitory computer-readable mediumof claim 8, wherein the first imaging scan and the second imaging scanare performed with one or more of magnetic resonance imaging (MRI),computerized tomography (CT), magnetoencephalography (MEG), or positronemission tomography (PET).
 10. The non-transitory computer-readablemedium of claim 8, wherein the sequence-adaptive multimodal segmentationalgorithm assigns a numeric label value to each voxel of the firstdataset or the second dataset.
 11. The non-transitory computer-readablemedium of claim 8, wherein the instructions are executable by the one ormore processors to: extract voxels from the first dataset having a labelcorresponding to a subcortical region of interest; form a third datasetcontaining the voxels extracted from the first dataset; convert thethird dataset into a first subcortical surface mesh model; computecurvature and sulcal features of the first subcortical surface meshmodel; align the first subcortical surface mesh model to a subcorticalatlas of the region of interest using the curvature and sulcal features;and overlay the first subcortical surface mesh model aligned to an atlasof the subcortical region of interest on a second subcortical surfacemesh model, the second subcortical surface mesh model having astandardized number of nodes that enables a one-to-one correspondencebetween node identity and atlas location; and assign coordinates ofnodes of the first subcortical_surface mesh model to the secondsubcortical structure surface mesh model such that the secondsubcortical_surface mesh model assumes a topology of the firstsubcortical structure surface mesh model.
 12. The non-transitorycomputer-readable medium of claim 8, wherein: the first imaging scan isa contrast weighted MRI scan and the first dataset is a contrastweighted dataset; and the instructions are executable by the one or moreprocessors to: select, based on the labeled dataset, voxels of the firstdataset identified as belonging to a cerebrospinal fluid region; apply amulti-scale tubular filtering algorithm to identify voxels of the firstdataset representing blood vessels and assign a vesselness weight valueto each voxel; integrate the vesselness weight values into the firstdataset; and after aligning the first dataset and the second dataset,convert the first dataset to a surface anatomical mesh model.
 13. Thenon-transitory computer-readable medium of claim 8, wherein: the firstimaging scan is a contrast weighted MRI scan and the second imaging scanis an anatomical MRI scan; and the instructions are executable by theone or more processors to: define predicted target point coordinates andentry point coordinates for a probe based on target point coordinatesand entry point coordinates of previously implanted probes; or by userdefined target and entry points; define a trajectory for the probe basedon a mean target coordinates and mean entry point coordinates; adjustthe trajectory to intersect with a nearest voxel assigned a label of ananatomical region of interest; check proximity of the trajectory tocritical structures based on user defined constraints and/or userdefined modification of the trajectory to satisfy the user definedconstraints; and superimpose the trajectory on the second data set toform a planning dataset.
 14. The non-transitory computer-readable mediumof claim 8, wherein: the first imaging scan is an anatomical MRI scan,the second imaging scan is a post-implantation CT imaging scan, thefirst dataset is an anatomical MRI dataset, and the second dataset is apost-implantation CT dataset; and the instructions are executable by theone or more processors to: obtain a third imaging scan used to guideelectrode implantation during surgery; convert the third imaging scan toa third dataset; align a third dataset with the first dataset; obtain atrajectory implant data file created during the electrode implantation;generate a planned trajectory dataset, based on trajectory implant datafile, that includes dummy objects disposed at locations of electrodegeometry; align the planned trajectory dataset to the post-implantationCT dataset; and automatically identify and label electrodes in the CTelectrode dataset based on the dummy objects of the trajectory implantdata file.
 15. A system, comprising: one or more processors; a memorycoupled to the one or more processors, wherein the memory storesinstructions that configure the one or more processors to: obtain afirst imaging scan and a second imaging scan of a subject brain; convertthe first imaging scan to a first dataset, and the second imaging scanto a second dataset; apply a sequence-adaptive multimodal segmentationalgorithm to the first dataset and the second dataset, wherein thesequence-adaptive multimodal segmentation algorithm performs automaticintensity-based tissue classification to generate a first labelleddataset and a second labeled dataset; automatically co-register thefirst labeled dataset and the second labeled dataset to each other togenerate a transformation matrix based on the first labeled dataset andthe second labeled dataset; and apply the transformation matrix to alignthe first dataset and the second dataset.
 16. The system of claim 15,wherein the first imaging scan and the second imaging scan are performedwith one or more of magnetic resonance imaging (MRI), computerizedtomography (CT), magnetoencephalography (MEG), or positron emissiontomography (PET).
 17. The system of claim 15, wherein thesequence-adaptive multimodal segmentation algorithm assigns a numericlabel value to each voxel of the first dataset or the second dataset.18. The system of claim 15, wherein the instructions configure the oneor more processors to: extract voxels from the first dataset having alabel corresponding to a subcortical region of interest; form a thirddataset containing the voxels extracted from the first dataset; convertthe third dataset into a first subcortical surface mesh model; computecurvature and sulcal features of the first subcortical surface meshmodel; align the first subcortical surface mesh model to a subcorticalatlas of the region of interest using the curvature and sulcal features;and overlay the first subcortical surface mesh model aligned to an atlasof the subcortical region of interest on a second subcortical surfacemesh model, the second subcortical surface mesh model having astandardized number of nodes that enables a one-to-one correspondencebetween node identity and atlas location; and assign coordinates ofnodes of the first subcortical_surface mesh model to the secondsubcortical structure surface mesh model such that the secondsubcortical_surface mesh model assumes a topology of the firstsubcortical structure surface mesh model.
 19. The system of claim 15,wherein: the first imaging scan is a contrast weighted MRI scan and thefirst dataset is a contrast weighted dataset; and the instructionsconfigure the one or more processors to: select, based on the labeleddataset, voxels of the first dataset identified as belonging to acerebrospinal fluid region; apply a multi-scale tubular filteringalgorithm to identify voxels of the first dataset representing bloodvessels and assign a vesselness weight value to each voxel; integratethe vesselness weight values into the first dataset; and after aligningthe first dataset and the second dataset, convert the first dataset to asurface anatomical mesh model.
 20. The system of claim 15, wherein: thefirst imaging scan is a contrast weighted MRI scan and the secondimaging scan is an anatomical MRI scan; and the instructions configurethe one or more processors to: define predicted target point coordinatesand entry point coordinates for a probe based on target pointcoordinates and entry point coordinates of previously implanted probes;or by user defined target and entry points; define a trajectory for theprobe based on a mean target coordinates and mean entry pointcoordinates; adjust the trajectory to intersect with a nearest voxelassigned a label of an anatomical region of interest; check proximity ofthe trajectory to critical structures based on user defined constraintsand/or user defined modification of the trajectory to satisfy the userdefined constraints; and superimpose the trajectory on the second dataset to form a planning dataset.
 21. The system of claim 15, wherein: thefirst imaging scan is an anatomical MRI scan, the second imaging scan isa post-implantation CT imaging scan, the first dataset is an anatomicalMRI dataset, and the second dataset is a post-implantation CT imagingdataset; and the instructions configure the one or more processors to:obtain a third imaging scan used to guide electrode implantation duringsurgery; convert the third imaging scan to a third dataset; align athird dataset with the first dataset; obtain a trajectory implant datafile created during the electrode implantation; generate a plannedtrajectory dataset, based on trajectory implant data file, that includesdummy objects disposed at locations of electrode geometry; align theplanned trajectory dataset to the post-implantation CT imaging dataset;and identify electrodes in the CT electrode dataset based on the dummyobjects of the trajectory implant data file.